Provides real life examples of how biased algorithms in big data can have serious effects; for example, how the "Compas" algorithm is used by courts to determine recidivism rates despite the possibility that the algorithm is racially discriminatory.

What happens when we fail to reject the null hypothesis.

Explains how almost no one predicted a high chance of Trump winning the 2016 Election.

Explains how rejecting the null hypothesis still does not explain the alternative.

Explains how splitting data into a classifier and training and test sets can help determine K (the number of similiar data points to be considered).